RICHFIELDS partners have published a new scientific paper. Automatic food image recognition systems are alleviating the process of food-intake estimation and dietary assessment. However, due to the nature of food images, their recognition is a particularly challenging task, which is why traditional approaches in the field have achieved a low classification accuracy. We present a novel approach to the problem of food and drink image detection and recognition that uses a newly-defined deep convolutional neural network architecture, called NutriNet.

Read the full paper (open-access!): Mezgec, S.; Koroušić Seljak, B. NutriNet: A Deep Learning Food and Drink Image Recognition System for Dietary Assessment. Nutrients 2017, 9, 657.

This work is supported by the project mHealth Platform for Parkinson’s Disease Management (PD_manager), which received funding from the European Union’s Horizon 2020 Research and Innovation program under Grant Number 643706. This work is also supported by the project Research Infrastructure on Consumer Health and Food Intake using E-Science with Linked Data Sharing (RICHFIELDS), which received funding from the European Union’s Horizon 2020 Research and Innovation program under Grant Number 654280. The authors acknowledge the financial support from the Slovenian Research Agency (Research Core Funding Number P2-0098).